[r/ML] [D] thoughts on current community moving away from heavy math?

Summary

The machine learning community is increasingly shifting away from heavy mathematical theory towards empirical findings, architectural designs, and system pipelines, a trend amplified by the rise of LLMs. While some areas like reinforcement learning still maintain a strong mathematical focus, a significant portion of current research, particularly post-LLM, involves integrating existing systems with minimal new mathematical contributions. This observation suggests a notable evolution in the preferred research methodologies within the field.

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